Indecate

Industrial Decarbonization Analysis Tool

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INDECATE is an analytical tool designed to support industry stakeholders, policymakers, and researchers in evaluating and comparing decarbonization pathways of industrial sectors through data-driven insights.

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Our expertise

Decarbonization analyses for various industrial sectors

Process blueprint development

Developing equation-oriented models of industrial processes, based on mass and energy balances

Decarbonization pathway screening

Comparing low-carbon alternatives, from fuel switching and electrification to heat integration and CO₂ capture

Techno-economic & environmental assessment

Quantifying the technical, environmental, and economic implications of decarbonization pathways

Decision support under uncertainty

Identifying robust and resilient decarbonization strategies under uncertain energy and carbon market conditions

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OUR METHODOLOGY

Data collection, modeling, and analysis

STEP 1

Data Collection

The data collection step gathers information on the industrial process under study and the relevant energy transition pathways. It draws on non-confidential sources, such as scientific literature, institutional reports, and BAT reference documents.

Industry Baseline
Definition

Process description, emissions baseline, production capacity, ...

Identification of Decarbonization Pathways

Fuel switching, electrification, CO₂ capture, waste valorization, energy efficiency

STEP 2

Modeling

The data collected in the previous step serve as the foundation for model development.

Process Blueprint Development

Equation-oriented models in Lua, incorporating mass and energy balances, and CAPEX/OPEX calculations

Superstructure Definition

Integration of alternative production options into a unified superstructure for comparison and optimization

STEP 3

Analysis

The analysis step combines environmental data with predefined economic scenarios from energy system operators and research institutes (Ecoinvent, Elia, ENTSO, EnergyVille,...), as well as user-defined scenarios. The following analyses can be performed.

Scenario-Based Assessment

Techno-economic comparison of alternative production routes based on various KPIs, including energy demand, carbon footprint, and specific cost

Parameter Sweep

MILP optimization across numerous pricing scenarios to map least-cost solutions and derive machine-learning-based technology selection rules

Uncertainty Quantification

Quantification of cost uncertainty by modeling price distributions and propagating uncertainty through the model using Polynomial Chaos Expansion.

Robustness Assessment

Technology ranking based on regret metrics, including mean regret and downside risk relative to the cost-optimal option